There is a fundamental gap in current Brain Machine Interface (BMI) technology, the lack of BMIs' abilities to work in real world situations, and to adapt with the user. We suggest the production of an autonomously adapting BMI. A key scientific aspect of our work is to further elucidate our preliminary findings on reward modulation of the primary motor cortex (M1). This finding indicates that we could generate our fully autonomous BMI using one implant in M1 with the use of reinforcement learning to update the BMI. Humans are currently being implanted in M1 with the same electrode arrays we are using, and thus we may be able to test our system in the short term. Our long-term biomedical engineering goal is to develop a fully integrated BMI that allows its user to recognize the system as self and make natural looking accurate movements. This will ultimately require a system that adapts with the user and provides sensory feedback. Our long-term neuroscience goals are to determine the basic properties of the primary sensorimotor cortices, such as the influence of reward on synaptic plasticity within these regions and sensorimotor adaptation and learning. The goals of this proposal are threefold, we will prove that supervised reinforcement learning (RL) leads to robust BMIs, that there is reward modulation in M1 and quantify how this modulation influences more traditional M1 representations, such as movement kinematics. Our Central Hypothesis is that reward modulates M1 neural activity and this modulation can be tapped into for the purpose of an autonomous BMI, which will learn to work with the user based on the users interpretation of the BMIs performance. This hypothesis is driven by our preliminary work showing that reinforcement-learning methods can produce good BMI control and that reward modulation has an influence on M1 activity. We have decoded reward expectation from M1 during reaching movements and passive observation, however, this was between reward and non-reward, and did not control for motivation explicitly as we now propose. Our specific aims are 1) examine if reward modulation has an influence on the primary motor cortex's representation of movement and action observation; 2) examine if there is an influence of reward modulation on the primary motor cortex under BMI control; and 3) examine the use of supervised reinforcement learning with a neurally derived evaluative signal for BMI control. Our contributions will be significant, in our opinion, because they will lead to enhanced BMIs that learn to work with the user for improved overall performance while adding significantly to our knowledge on M1. This should lead to increased independence for the BMI's user, and ultimately a decrease in health care costs. Not to mention an increased quality of life. The proposed research is innovative, in our opinion, because it pushes to move BMIs from the lab to real world capabilities, able to deal with changing environments autonomously. Our proposed work will help push us to new horizons ushering in the age of teaming between humans and altruistic computational agents.